EOS uses cookies so that we can offer you the best possible service. By continuing to use this website, you are agreeing to the storage and use of cookies on your computer. You can change this at any time by configuring the cookie settings.

Machine learning is coming into its own.

Looking for and finding solutions based on solid experience is not a bad idea. At EOS, that has worked very well for over 40 years. Until now, when a client handed over a receivable, debt collection systems would advise collection agents of the best course of action. They would send a letter to the defaulting payer, for example, because letters had achieved good results in past decades. Now, however, the company is combining its know-how in receivables management with algorithmic models and machine learning. Since June 2017, the EOS Group’s central access point for data analysis has been the Center of Analytics (CoA) in Hamburg, Germany. Patrick Witte, Team Manager Business Analytics at the CoA, explains its purpose: ‘In future we’ll be able to reach even more precise and objective decisions on how best to address an individual defaulting payer’.

In future we'll be able to reach even more precise and objective decisions.
Patrick Witte, Team Manager Business Analytics

Knowing in advance how things work.

‘The centrepiece of our system is the analytical platform. It ploughs through large data volumes at high speed and structures them’, says Joachim Göller, Head of the Center of Analytics. The platform consists of two parts. The segment used by developers contains data accumulated by EOS in the past. They are pseudonymised, which means no conclusions about individual defaulting payers can be drawn from them.

Data scientists seek patterns in order to programme models. For instance, such a model can predict the likelihood of a defaulting payer settling his outstanding amount within the next three months. Another model could calculate how great the prospects of success are when sending a letter. A third model could, for example, determine the rate of success of a telephone call. ‘With the aid of such models, we can initiate the best subsequent debt collection measure’, says Mr Witte.

How does digital debt collection work at EOS?

Preliminary examples at EOS show how that works in practice. For the analytical platform’s second area has begun working live with real receivables. Successfully tested models are put into practice here. They provide the inter-connected debt collection systems with information to determine the ideal next step in the debt recovery process.

‘In Germany, the Data Driven Decisions project is docked to the platform along with the new debt collection system “Best Next Inkasso”, which a project team is currently developing‘, explains Mr Witte. With ‘Best Next Inkasso’ EOS has started processing select receivables. ‘When we get a new receivable, the new system calculates in real time which measure is most likely to allow the collection agents to succeed the fastest with this particular receivable and this defaulting payer’, explains Mr Witte. Next, the model processes the information on whether the measure has worked. Through this machine learning, the forecasts are becoming increasingly accurate.

Plans call for other debt collection systems within the EOS Group to be connected to the platform by the end of 2018. The ‘Oyo’ system in France and the ‘Kollecto’ system in an Eastern European country will be added. Gradually, the company will initiate the most effective subsequent operation in the debt collection process. ‘By relying on solid data instead of only decades of experience, we can achieve even better results for our clients and their customers’, says Mr Göller.

This was the challenge: Data turn into information and decisions.

The Center of Analytics (CoA) in Hamburg, Germany, produces self-learning digital tools which evaluate and use pseudonymised data from the debt collection process. Based on the information obtained, sound decisions can be reached on which debt collection measure should be applied next in order to achieve the best results. Data analyses thus improve debt collection processes and results